Search results for: 99.95% IoT data transmission savings

Authors: Mangaka C. Matoetoe, Fredrick O. Okumu

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Metal nanoparticles have attracted a great interest in scientific research and industrial applications, owing to their unique large surface area-to-volume ratios and quantum-size effects. Supported metal nanoparticles play a pivotal role in areas such as nanoelectronics, energy storage and as catalysts for the sustainable production of fuels and chemicals. Monometallics (Ag, Pt) and Silver-platinum (Ag-Pt) bimetallic (BM) nanoparticles (NPs) with a mole fraction (1:1) were prepared by reduction / co-reduction of hexachloroplatinate and silver nitrate with sodium citrate. The kinetics of the nanoparticles formation was monitored using UV-visible spectrophotometry. Transmission electron microscopy (TEM) and Energy-dispersive X-ray (EDX) spectroscopy were used for size, film morphology as well as elemental composition study. Fast reduction processes was noted in Ag NPs (0.079 s-1) and Ag-Pt NPs 1:1 (0.082 s-1) with exception of Pt NPs (0.006 s-1) formation. The UV-visible spectra showed characteristic peaks in Ag NPs while the Pt NPs and Ag-Pt NPs 1:1 had no observable absorption peaks. UV visible spectra confirmed chemical reduction resulting to formation of NPs while TEM images depicted core-shell arrangement in the Ag-Pt NPs 1:1 with particle size of 20 nm. Monometallic Ag and Pt NPs reported particle sizes of 60 nm and 2.5 nm respectively. The particle size distribution in the BM NPs was found to directly depend on the concentration of Pt NPs around the Ag core. EDX elemental composition analysis of the nanoparticle suspensions confirmed presence of the Ag and Pt in the Ag-Pt NPs 1:1. All the spectroscopic analysis confirmed the successful formation of the nanoparticles.

Keywords: kinetics, morphology, nanoparticles, platinum, silver

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Authors: Robert Joseph M. Licup

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The deployment of the fifth-generation (5G) mobile system through mmWave frequencies is the new solution in the requirement to provide higher bandwidth readily available for all users. The usage pattern of the mobile users has moved towards either the work from home or online classes set-up because of the pandemic. Previous mobile technologies can no longer meet the high speed, and bandwidth requirement needed, given the drastic shift of transactions to the home. The millimeter-wave (mmWave) underutilized frequency is utilized by the fifth-generation (5G) cellular networks that support multi-gigabit-per-second (Gbps) transmission. However, due to its short wavelengths, high path loss, directivity, blockage sensitivity, and narrow beamwidth are some of the technical challenges that need to be addressed. Different tools, technologies, and scenarios are explored to support network design, accurate channel modeling, implementation, and deployment effectively. However, there is a big challenge on how the consumer will adopt this solution and maximize the benefits offered by the 5G Technology. This research proposes to study the intricacies of technology diffusion, individual attitude, behaviors, and how technology adoption will be attained. The agent based simulation model shaped by the actual applications, technology solution, and related literature was used to arrive at a computational model. The research examines the different attributes, factors, and intricacies that can affect each identified agent towards technology adoption.

Keywords: agent-based model, AnyLogic, 5G O21, 5G mmWave solutions, technology adoption

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Authors: Jude Opara, Esemokumo Perewarebo Akpos

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This paper is on non-parametric linear regression over its parametric counterparts with large sample size. Data set on anthropometric measurement of primary school pupils was taken for the analysis. The study used 50 randomly selected pupils for the study. The set of data was subjected to normality test, and it was discovered that the residuals are not normally distributed (i.e. they do not follow a Gaussian distribution) for the commonly used least squares regression method for fitting an equation into a set of (x,y)-data points using the Anderson-Darling technique. The algorithms for the nonparametric Theil’s regression are stated in this paper as well as its parametric OLS counterpart. The use of a programming language software known as “R Development” was used in this paper. From the analysis, the result showed that there exists a significant relationship between the response and the explanatory variable for both the parametric and non-parametric regression. To know the efficiency of one method over the other, the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) are used, and it is discovered that the nonparametric regression performs better than its parametric regression counterparts due to their lower values in both the AIC and BIC. The study however recommends that future researchers should study a similar work by examining the presence of outliers in the data set, and probably expunge it if detected and re-analyze to compare results.

Keywords: Theil’s regression, Bayesian information criterion, Akaike information criterion, OLS

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Authors: D. Prangchumpol

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This research presents a forecasting method of requisition document demands for Military units by using Exponential Smoothing methods to analyze data. The data used in the forecast is an actual data requisition document of The Adjutant General Department. The results of the forecasting model to forecast the requisition of the document found that Holt–Winters’ trend and seasonality method of α=0.1, β=0, γ=0 is appropriate and matches for requisition of documents. In addition, the researcher has developed a requisition online system to improve the performance of requisition documents of The Adjutant General Department, and also ensuring that the operation can be checked.

Keywords: requisition, holt–winters, time series, royal thai army

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Authors: Ibrahim Mohammed, Suleiman Taofiq, Muhammad Naziru Yahya

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Geoelectrical measurements using Schlumberger Vertical Electrical Sounding (VES) method were carried out in Waziri Umaru Federal Polytechnic, Birnin Kebbi, Nigeria, with the aim of determining the groundwater potential in the area. Twelve (12) Vertical Electric Sounding (VES) data were collected using Terrameter (ABEM SAS 300c) and analyzed using computer software (IPI2win), which gives an automatic interpretation of the apparent resistivity. The results of the interpretation of VES data were used in the characterization of three to five geo-electric layers from which the aquifer units were delineated. Data analysis indicated that water bearing formation exists in the third and fourth layers having resistivity range of 312 to 767 Ωm and 9.51 to 681 Ωm, respectively. The thickness of the formation ranges from 14.7 to 41.8 m, while the depth is from 8.22 to 53.7 m. Based on the result obtained from the interpretation of the data, five (5) VES stations were recommended as the most viable locations for groundwater exploration in the study area. The VES stations include VES A4, A5, A6, B1, and B2. The VES results of the entire area indicated that the water bearing formation occurs at maximum depth of 53.7 m at the time of this survey.

Keywords: aquifer, depth, groundwater, resistivity, Schlumberger

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Authors: Dalvin D. Hill, Hector M. Castro Garcia

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A growing number of individuals utilize wearable devices on a daily basis. The usage and functionality of these wearable devices vary from user to user. One popular usage of said devices is to track health-related activities that are typically stored on a device’s memory or uploaded to an account in the cloud; based on the current trend, the data accumulated from the wearable device are stored in a standalone location. In many of these cases, this health related datum is not a factor when considering the holistic view of a user’s health lifestyle or record. This health-related data generated from wearable and Internet of Things (IoT) devices can serve as empirical information to a medical provider, as the standalone data can add value to the holistic health record of a patient. This paper proposes a solution to incorporate the data gathered from these wearable and IoT devices, with that a patient’s Personal Health Record (PHR) stored within the confines of a Health Information Exchange (HIE).

Keywords: electronic health record, health information exchanges, internet of things, personal health records, wearable devices, wearables

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Authors: Chao Yu, Qing-Guo Wang, Dan Zhang

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The outlier detection problem for dynamic systems is formulated as a matrix decomposition problem with low-rank, sparse matrices and further recast as a semidefinite programming (SDP) problem. A fast algorithm is presented to solve the resulting problem while keeping the solution matrix structure and it can greatly reduce the computational cost over the standard interior-point method. The computational burden is further reduced by proper construction of subsets of the raw data without violating low rank property of the involved matrix. The proposed method can make exact detection of outliers in case of no or little noise in output observations. In case of significant noise, a novel approach based on under-sampling with averaging is developed to denoise while retaining the saliency of outliers and so-filtered data enables successful outlier detection with the proposed method while the existing filtering methods fail. Use of recovered “clean” data from the proposed method can give much better parameter estimation compared with that based on the raw data.

Keywords: outlier detection, system identification, matrix decomposition, low-rank matrix, sparsity, semidefinite programming, interior-point methods, denoising

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Authors: Walter Bernhofer, Peter Haber, Tobias Mayer, Manfred Mayr, Markus Ziegler

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Current preventive maintenance measures are cost intensive and not efficient. With the available sensor data of state of the art internet of things devices new possibilities of automated data processing emerge. Current advances in data science and in machine learning enable new, so called predictive maintenance technologies, which empower data scientists to forecast possible system failures. The goal of this approach is to cut expenses in preventive maintenance by automating the detection of possible failures and to improve efficiency and quality of maintenance measures. Additionally, a centralization of the sensor data monitoring can be achieved by using this approach. This paper describes the approach of three students to define a reference architecture for a predictive maintenance solution in the internet of things domain with a connected smartphone app for service technicians. The reference architecture is validated by a case study. The case study is implemented with current Microsoft Azure cloud technologies. The results of the case study show that the reference architecture is valid and can be used to achieve a system for predictive maintenance execution with the cloud components of Microsoft Azure. The used concepts are technology platform agnostic and can be reused in many different cloud platforms. The reference architecture is valid and can be used in many use cases, like gas station maintenance, elevator maintenance and many more.

Keywords: case study, internet of things, predictive maintenance, reference architecture

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Authors: Judith Mwakalonge, Geophrey Mbatta, Saidi Siuhi, Gurcan Comert, Cuthbert Ruseruka

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Transportation agencies worldwide continuously monitor their roads' conditions to minimize road maintenance costs and maintain public safety and rideability quality. Existing methods for carrying out road condition surveys involve manual observations of roads using standard survey forms done by qualified road condition surveyors or engineers either on foot or by vehicle. Automated road condition survey vehicles exist; however, they are very expensive since they require special vehicles equipped with sensors for data collection together with data processing and computing devices. The manual methods are expensive, time-consuming, infrequent, and can hardly provide real-time information for road conditions. This study contributes to this arena by utilizing built-in vehicle technologies, drones, and deep learning to automate road condition surveys while using low-cost technology. A single model is trained to capture flexible pavement distresses (Potholes, Rutting, Cracking, and raveling), thereby providing a more cost-effective and efficient road condition monitoring approach that can also provide real-time road conditions. Additionally, data fusion is employed to enhance the road condition assessment with data from vehicles and drones.

Keywords: road conditions, built-in vehicle technology, deep learning, drones

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Authors: Sanjeet Kumar Singh, Shanatanu Bhattacharaya

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Design of high- efficiency low, frequency (<1000Hz) soundproof window or wall absorber which is transparent to airflow is presented. Due to the massive rise in human population and modernization, environmental noise has significantly risen globally. Prolonged noise exposure can cause severe physiological and psychological symptoms like nausea, headaches, fatigue, and insomnia. There has been continuous growth in building construction and infrastructure like offices, bus stops, and airports due to urban population. Generally, a ventilated window is used for getting fresh air into the room, but at the same time, unwanted noise comes along. Researchers used traditional approaches like noise barrier mats in front of the window or designed the entire window using sound-absorbing materials. However, this solution is not aesthetically pleasing, and at the same time, it's heavy and not adequate for low-frequency noise shielding. To address this challenge, we design a transparent hexagonal panel based on Sierpiński fractal triangle, which is aesthetically pleasing, demonstrates normal incident sound absorption coefficient more than 0.96 around 700 Hz and transmission loss around 23 dB while maintaining e air circulation through triangular cutout. Next, we present a concept of fabrication of large acoustic panel for large-scale applications, which lead to suppressing the urban noise pollution.

Keywords: acoustic metamaterials, noise, functional materials, ventilated

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Authors: Thien Van Ngo

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The engineering design process is a systematic approach to solving problems. It involves identifying a problem, brainstorming solutions, prototyping and testing solutions, and evaluating the results. The engineering design process can be used to teach students how to solve problems in a creative and innovative way. The research aim of this study was to investigate the effectiveness of using the engineering design process to enhance student learning outcomes in a physics course. A mixed research method was used in this study. The quantitative data were collected using a pretest-posttest control group design. The qualitative data were collected using semi-structured interviews. The sample was 150 first-year students in the Department of Mechanical Engineering Technology at Cao Thang Technical College in Vietnam in the 2022-2023 school year. The quantitative data were collected using a pretest-posttest control group design. The pretest was administered to both groups at the beginning of the study. The posttest was administered to both groups at the end of the study. The qualitative data were collected using semi-structured interviews with a sample of eight students in the experimental group. The interviews were conducted after the posttest. The quantitative data were analyzed using independent sample T-tests. The qualitative data were analyzed using thematic analysis. The quantitative data showed that students in the experimental group, who were taught using the engineering design process, had significantly higher post-test scores on physics problem-solving than students in the control group, who were taught using the conventional method. The qualitative data showed that students in the experimental group were more motivated and engaged in the learning process than students in the control group. Students in the experimental group also reported that they found the engineering design process to be a more effective way of learning physics. The findings of this study suggest that the engineering design process can be an effective way of enhancing student learning outcomes in physics courses. The engineering design process engages students in the learning process and helps them to develop problem-solving skills.

Keywords: engineering design process, problem-solving, learning outcome of physics, students’ physics competencies, deep learning

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Authors: Jalal Haghighat Monfared, Zahra Akbari

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Today, business executives need to have useful information to make better decisions. Banks have also been using information tools so that they can direct the decision-making process in order to achieve their desired goals by rapidly extracting information from sources with the help of business intelligence. The research seeks to investigate whether there is a relationship between the quality of decision making and the business intelligence capabilities of Mellat Bank. Each of the factors studied is divided into several components, and these and their relationships are measured by a questionnaire. The statistical population of this study consists of all managers and experts of Mellat Bank's General Departments (including 190 people) who use commercial intelligence reports. The sample size of this study was 123 randomly determined by statistical method. In this research, relevant statistical inference has been used for data analysis and hypothesis testing. In the first stage, using the Kolmogorov-Smirnov test, the normalization of the data was investigated and in the next stage, the construct validity of both variables and their resulting indexes were verified using confirmatory factor analysis. Finally, using the structural equation modeling and Pearson's correlation coefficient, the research hypotheses were tested. The results confirmed the existence of a positive relationship between decision quality and business intelligence capabilities in Mellat Bank. Among the various capabilities, including data quality, correlation with other systems, user access, flexibility and risk management support, the flexibility of the business intelligence system was the most correlated with the dependent variable of the present research. This shows that it is necessary for Mellat Bank to pay more attention to choose the required business intelligence systems with high flexibility in terms of the ability to submit custom formatted reports. Subsequently, the quality of data on business intelligence systems showed the strongest relationship with quality of decision making. Therefore, improving the quality of data, including the source of data internally or externally, the type of data in quantitative or qualitative terms, the credibility of the data and perceptions of who uses the business intelligence system, improves the quality of decision making in Mellat Bank.

Keywords: business intelligence, business intelligence capability, decision making, decision quality

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Authors: Rahul Patel

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Ahmedabad, a city located in western India, is experiencing rapid growth due to urbanization and industrialization. It is projected to become a metropolitan city in the near future, resulting in various construction activities. Soil testing is necessary before construction can commence, requiring construction companies and contractors to periodically conduct soil testing. The focus of this study is on the process of creating a spatial database that is digitally formatted and integrated with geotechnical data and a Geographic Information System (GIS). Building a comprehensive geotechnical (Geo)-database involves three steps: collecting borehole data from reputable sources, verifying the accuracy and redundancy of the data, and standardizing and organizing the geotechnical information for integration into the database. Once the database is complete, it is integrated with GIS, allowing users to visualize, analyze, and interpret geotechnical information spatially. Using a Topographic to Raster interpolation process in GIS, estimated values are assigned to all locations based on sampled geotechnical data values. The study area was contoured for SPT N-Values, Soil Classification, Φ-Values, and Bearing Capacity (T/m2). Various interpolation techniques were cross-validated to ensure information accuracy. This GIS map enables the calculation of SPT N-Values, Φ-Values, and bearing capacities for different footing widths and various depths. This study highlights the potential of GIS in providing an efficient solution to complex phenomena that would otherwise be tedious to achieve through other means. Not only does GIS offer greater accuracy, but it also generates valuable information that can be used as input for correlation analysis. Furthermore, this system serves as a decision support tool for geotechnical engineers.

Keywords: ArcGIS, borehole data, geographic information system, geo-database, interpolation, SPT N-value, soil classification, Φ-Value, bearing capacity

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Authors: B. R. Shen, J. R. Wang, J. H. Yang, S. W. Chen, C. Shih, Y. Chiang, Y. F. Chang, Y. H. Huang

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In this research, TRACE code with the interface code-SNAP was used to simulate and analyze the SBO (station blackout) accident which occurred in Maanshan PWR (pressurized water reactor) nuclear power plant (NPP). There are four main steps in this research. First, the SBO accident data of Maanshan NPP were collected. Second, the TRACE/SNAP model of Maanshan NPP was established by using these data. Third, this TRACE/SNAP model was used to perform the simulation and analysis of SBO accident. Finally, the simulation and analysis of SBO with mitigation equipments was performed. The analysis results of TRACE are consistent with the data of Maanshan NPP. The mitigation equipments of Maanshan can maintain the safety of Maanshan in the SBO according to the TRACE predictions.

Keywords: pressurized water reactor (PWR), TRACE, station blackout (SBO), Maanshan

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Authors: L. M. Ombaka, R. S. Oosthuizen, P. G. Ndungu, V. O. Nyamori

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Understanding the role of nitrogen species on the catalytic properties of nitrogen-doped carbon nanotubes (N-CNTs) as catalysts supports is critical as nitrogen species influence the support’s properties. To evaluate the influence of pyrrolic nitrogen on the physicochemical properties and catalytic activity of N-CNTs supported Pd (Pd/N-CNTs); N-CNTs containing varying pyrrolic contents were synthesized. The catalysts were characterised by the use of transmission electron microscope (TEM), scanning electron microscope, X-ray photoelectron spectroscopy (XPS), X-ray diffraction, Fourier transform infrared spectroscopy, and temperature programmed reduction. TEM analysis showed that the Pd nanoparticles were mainly located along the defect sites on N-CNTs. XPS analysis revealed that the abundance of Pd0 decreased while that of Pd2+ increased as the quantity of pyrrolic nitrogen increased. The increase of Pd2+ species was accredited to the formation of stable Pd-N coordination complexes which prevented further reduction of Pd2+ to Pd0 during synthesis. The formed Pd-N complexes increased the stability and dispersion of Pd2+ nanoparticles. The selective hydrogenation of nitrobenzophenone to aminobenzophenone over Pd/N-CNTs was compared to that of Pd on carbon nanotubes (Pd/CNTs). Pd/N-CNTs showed a higher catalytic activity and selectivity compared with Pd/CNTs. Pyrrolic nitrogen functional groups significantly promoted the selectivity towards aminobenzophenone formation.

Keywords: pyrrolic N-CNTs, hydrogenation reactions, chemical vapour deposition technique

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Authors: Jojo Y. C. Mo

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Memories are good. They remind us of people, places and experiences that we cherish. But memories cannot be changed and there may well be memories that we do not want to remember. This is particularly true in relation to information which causes us embarrassment and humiliation or simply because it is private – we all want to erase or delete such information. This desire to delete is recently recognised by the Court of Justice of the European Union in the 2014 case of Google Spain SL, Google Inc. v Agencia Española de Protección de Datos, Mario Costeja González in which the court ordered Google to remove links to some information about the complainant which he wished to be removed. This so-called ‘right to be forgotten’ received serious attention and significantly, the European Council and the European Parliament enacted the General Data Protection Regulation (GDPR) to provide a more structured and normative framework for implementation of right to be forgotten across the EU. This development in data protection laws will, undoubtedly, have significant impact on companies and co-operations not just within the EU but outside as well. Hong Kong, being one of the world’s leading financial and commercial center as well as one of the first jurisdictions in Asia to implement a comprehensive piece of data protection legislation, is therefore a jurisdiction that is worth looking into. This article/project aims to investigate the following: a) whether there is a right to be forgotten under the existing Hong Kong data protection legislation b) if not, whether such a provision is necessary and why. This article utilises a comparative methodology based on a study of primary and secondary resources, including scholarly articles, government and law commission reports and working papers and relevant international treaties, constitutional documents, case law and legislation. The author will primarily engage literature and case-law review as well as comparative and doctrinal analyses. The completion of this article will provide privacy researchers with more concrete principles and data to conduct further research on privacy and data protection in Hong Kong and internationally and will provide a basis for policy makers in assessing the rationale and need for a right to be forgotten in Hong Kong.

Keywords: privacy, right to be forgotten, data protection, Hong Kong

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Authors: Hyeongju Jeon, Yonghyun Kim, Yongil Kim

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Synthetic Aperture Radar (SAR) is an effective tool for damage assessment induced by disasters due to its all-weather and night/day acquisition capability. In this paper, the 2017 Colombia landslide was observed using full-polarimetric ALOS/PALSAR-2 data. Polarimetric decompositions, including the Freeman-Durden decomposition and the Cloude decomposition, are utilized to analyze the scattering mechanisms changes before and after-landslide. These analyses are used to detect the damaged areas induced by the landslide. Experimental results validate the efficiency of the full polarimetric SAR data since the damaged areas can be well discriminated. Thus, we can conclude the proposed method using full polarimetric data has great potential for damage assessment of landslides.

Keywords: Synthetic Aperture Radar (SAR), polarimetric decomposition, damage assessment, landslide

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Authors: Sofia Stoica

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In this paper, we use historical data to predict the stock price of a tech company. To this end, we use a dataset consisting of the stock prices in the past five years of ten major tech companies – Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We experimented with a variety of models– a linear regressor model, K nearest Neighbors (KNN), a sequential neural network – and algorithms - Multiplicative Weight Update, and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement.

Keywords: finance, machine learning, opening price, stock market

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Authors: Jihen Bennaceur, Wissem Zouaghi, Ali Mabrouk

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The major aim of cyber threat intelligence (CTI) is to provide sophisticated knowledge about cybersecurity threats to ensure internal and external safeguards against modern cyberattacks. Inaccurate, incomplete, outdated, and invaluable threat intelligence is the main problem. Therefore, data analysis based on AI algorithms is one of the emergent solutions to overcome the threat of information-sharing issues. In this paper, we propose a supervised machine learning-based algorithm to improve threat information sharing by providing a sophisticated classification of cyber threats and data. Extensive simulations investigate the accuracy, precision, recall, f1-score, and support overall to validate the designed algorithm and to compare it with several supervised machine learning algorithms.

Keywords: threat information sharing, supervised learning, data classification, performance evaluation

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Authors: Bal Chandra Yadav, Rakesh K. Sonker, Anjali Sharma, Punit Tyagi, Vinay Gupta, Monika Tomar

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In the present study, ZnO doped SnO2 thin films of various compositions were deposited on the surface of a corning substrate by dropping the two sols containing the precursors for composite (ZSO) with subsequent heat treatment. The sensor materials used for selective detection of nitrogen dioxide (NO2) were designed from the correlation between the sensor composition and gas response. The available NO2 sensors are operative at very high temperature (150-800 °C) with low sensing response (2-100) even in higher concentrations. Efforts are continuing towards the development of NO2 gas sensor aiming with an enhanced response along with a reduction in operating temperature by incorporating some catalysts or dopants. Thus in this work, a novel sensor structure based on ZSO nanocomposite has been fabricated using chemical route for the detection of NO2 gas. The structural, surface morphological and optical properties of prepared films have been studied by using X-ray diffraction (XRD), Atomic force microscopy (AFM), Transmission electron microscope (TEM) and UV-visible spectroscopy respectively. The effect of thickness variation from 230 nm to 644 nm of ZSO composite thin film has been studied and the ZSO thin film of thickness ~ 460 nm was found to exhibit the maximum gas sensing response ~ 2.1×103 towards 20 ppm NO2 gas at an operating temperature of 90 °C. The average response and recovery times of the sensor were observed to be 3.51 and 6.91 min respectively. Selectivity of the sensor was checked with the cross-exposure of vapour CO, acetone, IPA, CH4, NH3 and CO2 gases. It was found that besides the higher sensing response towards NO2 gas, the prepared ZSO thin film was also highly selective towards NO2 gas.

Keywords: ZSO nanocomposite thin film, ZnO tetrapod structure, NO2 gas sensor, sol-gel method

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Authors: A. Asgharzadeh, M. Maroufi

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5G mobile communication system has drawn more and more attention. The 5G system needs to provide three different types of services, including enhanced Mobile BroadBand (eMBB), massive machine-type communication (mMTC), and ultra-reliable and low-latency communication (URLLC). Universal Filtered Multi-Carrier (UFMC), Filter Bank Multicarrier (FBMC), and Filtered Orthogonal Frequency Division Multiplexing (f-OFDM) are suggested as a well-known candidate waveform for the coming 5G system. Themachine-to-machine (M2M) communications are one of the essential applications in 5G, and it involves exchanging of concise messages with a very short latency. However, in UFMC systems, the subcarriers are grouped into subbands but f-OFDM only one subband covers the entire band. Furthermore, in FBMC, a subband includes only one subcarrier, and the number of subbands is the same as the number of subcarriers. This paper mainly discusses the performance of UFMC with different parameters for the UFMC system. Also, paper shows that UFMC is the best choice outperforming OFDM in any case and FBMC in case of very short packets while performing similarly for long sequences with channel estimation techniques for Interleave Division Multiple Access (IDMA) systems.

Keywords: universal filtered multi-carrier technique, UFMC, interleave division multiple access, IDMA, fifth-generation, subband

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Authors: Patricia Portugal Marques de Carvalho Lourenco

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A three-phase methodological multi-lingual path was designed, constructed and carried out using the 2020 Chinese Internet Drama Series We Are All Alone as a case study. Phase one, the backbone of the research, comprised of secondary data analysis, providing the structure on which the next two phases would be built on. Phase one incorporated a Google Scholar and a Baidu Index analysis, Star Network Influence Index and Mydramalist.com top two drama reviews, along with an article written about the drama and scrutiny of Chinese related blogs and websites. Phase two was field research elaborated across Latin Europe, and phase three was social media focused, having into account that perceptions are going to be memory conditioned based on past ideas recall. Overall, research has shown the poor cultural expression of Chinese entertainment in Latin Europe and demonstrated the inexistence of Chinese content in French, Italian, Portuguese and Spanish Business to Consumer retailers; a reflection of their low significance in Latin European markets and the short-life cycle of entertainment products in general, bubble-gum, disposable goods without a mid to long-term effect in consumers lives. The process of conducting comprehensive international research was complex and time-consuming, with data not always available in Mandarin, the researcher’s linguistic deficiency, limited Chinese Cultural Knowledge and cultural equivalence. Despite steps being taken to minimize the international proposed research, theoretical limitations concurrent to Latin Europe and China still occurred. Data accuracy was disputable; sampling, data collection/analysis methods are heterogeneous; ascertaining data requirements and the method of analysis to achieve a construct equivalence was challenging and morose to operationalize. Secondary data was also not often readily available in Mandarin; yet, in spite of the array of limitations, research was done, and results were produced.

Keywords: research methodologies, international research, primary data, secondary data, research limitations, online dramas, china, latin europe

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Authors: Liudmila Shabalinskaya, Victor Golovanov, Liudmila Milinis, Sergey Loponos, Alexander Maslov, D. O. Frolov

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The work is devoted to the rapid laboratory diagnosis of the condition of aircraft friction units, based on the application of the nondestructive testing method by analyzing the parameters of wear particles, or tribodiagnostics. The most important task of tribodiagnostics is to develop recommendations for the selection of more advanced designs, materials and lubricants based on data on wear processes for increasing the life and ensuring the safety of the operation of machines and mechanisms. The object of tribodiagnostics in this work are the tooth gears of the central drive and the gearboxes of the gas turbine engine of the civil aviation PS-90A type, in which rolling friction and sliding friction with slip occur. The main criterion for evaluating the technical state of lubricated friction units of a gas turbine engine is the intensity and rate of wear of the friction surfaces of the friction unit parts. When the engine is running, oil samples are taken and the state of the friction surfaces is evaluated according to the parameters of the wear particles contained in the oil sample, which carry important and detailed information about the wear processes in the engine transmission units. The parameters carrying this information include the concentration of wear particles and metals in the oil, the dispersion composition, the shape, the size ratio and the number of particles, the state of their surfaces, the presence in the oil of various mechanical impurities of non-metallic origin. Such a morphological analysis of wear particles has been introduced into the order of monitoring the status and diagnostics of various aircraft engines, including a gas turbine engine, since the type of wear characteristic of the central drive and the drive box is surface fatigue wear and the beginning of its development, accompanied by the formation of microcracks, leads to the formation of spherical, up to 10 μm in size, and in the aftermath of flocculent particles measuring 20-200 μm in size. Tribodiagnostics using the morphological analysis of wear particles includes the following techniques: ferrography, filtering, and computer analysis of the classification and counting of wear particles. Based on the analysis of several series of oil samples taken from the drive box of the engine during their operating time, a study was carried out of the processes of wear kinetics. Based on the results of the study and comparing the series of criteria for tribodiagnostics, wear state ratings and statistics of the results of morphological analysis, norms for the normal operating regime were developed. The study allowed to develop levels of wear state for friction surfaces of gearing and a 10-point rating system for estimating the likelihood of the occurrence of an increased wear mode and, accordingly, prevention of engine failures in flight.

Keywords: aviation, box of drives, morphological analysis, tribodiagnostics, tribology, ferrography, filtering, wear particle

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Authors: Mansour F. Hussein, Mohammed A. Alshaikh, Riyadh S. Al-Jumaah, A. GarelNabi, I. Al-Khalifa, Osama B. Mohammed

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Serum samples from 489 male and female camels were tested for antibodies against C. burnetii using indirect enzyme-linked immunosorbent assay (ELISA). Antibodies to C. burnetii were recorded in sera of 252 (51.64%) camels. Significant differences in prevalence were found between male and female camels, juvenile and adult camels, different ecotypes and different sampling locations. 307 camels were simultaneously tested for C. burnetii antibodies by ELISA and indirect immunofluorescence (IFA). Close agreement was found between the results of the two tests. A high prevalence of C. burnetii antibodies was also recorded in milk samples tested by ELISA. Clinical samples from serologically positive camels were subjected to PCR analysis using primers which amplify the repetitive transposon-like and transposase gene regions of C. burnetii. Positive DNA amplification was obtained from both regions, with highest shedding of C. burnetii in faecal samples (27.59%) followed, in descending order, by urine (23.81%), blood (15.85%) and milk (6.5%). The present results indicate that camels are a major reservoir of C. burnetii in Saudi Arabia. The high prevalence of infection in camels, the poor sanitary standards under which the animals are kept and the consumption of raw camel milk indicate that camels could also be a major source of transmission of Q fever to humans in Saudi Arabia.

Keywords: Arabian camel, Camelus dromedarius, Coxiella brunetii, ELISA, immunofluoresence, PCR

Procedia PDF Downloads 653

Authors: Tariq Ahamad, Mohammed S. Al-Kahtani, Taisir Eldos

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Optical communications technology has made enormous and steady progress for several decades, providing the key resource in our increasingly information-driven society and economy. Much of this progress has been in finding innovative ways to increase the data carrying capacity of a single optical fiber. In this research article we have explored basic issues in terms of security and reliability for secure and reliable information transfer through the fiber infrastructure. Conspicuously, one potentially enormous source of improvement has however been left untapped in these systems: fibers can easily support hundreds of spatial modes, but today’s commercial systems (single-mode or multi-mode) make no attempt to use these as parallel channels for independent signals. Bandwidth, performance, reliability, cost efficiency, resiliency, redundancy, and security are some of the demands placed on telecommunications today. Since its initial development, fiber optic systems have had the advantage of most of these requirements over copper-based and wireless telecommunications solutions. The largest obstacle preventing most businesses from implementing fiber optic systems was cost. With the recent advancements in fiber optic technology and the ever-growing demand for more bandwidth, the cost of installing and maintaining fiber optic systems has been reduced dramatically. With so many advantages, including cost efficiency, there will continue to be an increase of fiber optic systems replacing copper-based communications. This will also lead to an increase in the expertise and the technology needed to tap into fiber optic networks by intruders. As ever before, all technologies have been subject to hacking and criminal manipulation, fiber optics is no exception. Researching fiber optic security vulnerabilities suggests that not everyone who is responsible for their networks security is aware of the different methods that intruders use to hack virtually undetected into fiber optic cables. With millions of miles of fiber optic cables stretching across the globe and carrying information including but certainly not limited to government, military, and personal information, such as, medical records, banking information, driving records, and credit card information; being aware of fiber optic security vulnerabilities is essential and critical. Many articles and research still suggest that fiber optics is expensive, impractical and hard to tap. Others argue that it is not only easily done, but also inexpensive. This paper will briefly discuss the history of fiber optics, explain the basics of fiber optic technologies and then discuss the vulnerabilities in fiber optic systems and how they can be better protected. Knowing the security risks and knowing the options available may save a company a lot embarrassment, time, and most importantly money.

Keywords: in-house networks, fiber optics, security risk, money

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Authors: Srijanani Anurag Prasad

Abstract:

The Coalescence Hidden-variable Fractal Interpolation Surface (CHFIS) was built by combining interpolation data from the Iterated Function System (IFS). The interpolation data in a CHFIS comprises a row and/or column of uncertain values when a single point is entered. Alternatively, a row and/or column of additional points are placed in the given interpolation data to demonstrate the node added CHFIS. There are three techniques for inserting new points that correspond to the row and/or column of nodes inserted, and each method is further classified into four types based on the values of the inserted nodes. As a result, numerous forms of node insertion can be found in a CHFIS.

Keywords: fractal, interpolation, iterated function system, coalescence, node insertion, knot insertion

Procedia PDF Downloads 101

Authors: Melvin Chan, Chew Lee Teo

Abstract:

There has been a growing emphasis in elevating the teachers’ proficiency and competencies through continuing professional development (CPD) opportunities. In this era of a Volatile, Uncertain, Complex, Ambiguous (VUCA) world, teachers are expected to be collaborative designers, critical thinkers and creative builders. However, many of the CPD structures are still revolving in the model of transmission, which stands in contradiction to the cultivation of future-ready teachers for the innovative world of emerging technologies. This article puts forward the framing of CPD through a Principle-Based, Technological-Driven Knowledge Building Approach grounded in the essence of andragogy and progressive learning theories where growth is best exemplified through an authentic immersion in a social/community experience-based setting. Putting this Knowledge Building Professional Development Model (KBPDM) in operation via a Professional Learning Team (PLT) situated in a Secondary School in Singapore, research findings reveal that the intervention has led to a fundamental change in the learning paradigm of the teachers, henceforth equipping and empowering them successfully in their pedagogical design and practices for a 21^st century classroom experience. This article concludes with the possibility in leveraging the Learning Analytics to deepen the CPD experiences for educators.

Keywords: continual professional development, knowledge building, learning paradigm, principle-based

Procedia PDF Downloads 130

Authors: Moses Emetere, Marvel Akinyemi, S. E. Sanni

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At the moment, AERONET or AMMA database shows a large volume of data loss. With only about 47% data set available to the scientist, it is evident that accurate nowcast or forecast cannot be guaranteed. The calibration constants of most radiosonde or weather stations are not compatible with the atmospheric conditions of the West African climate. A dispersion model was developed to incorporate salient mathematical representations like a Unified number. The Unified number was derived to describe the turbulence of the aerosols transport in the frictional layer of the lower atmosphere. Fourteen years data set from Multi-angle Imaging SpectroRadiometer (MISR) was tested using the dispersion model. A yearly estimation of the atmospheric constants over Ouagadougou using the model was obtained with about 87.5% accuracy. It further revealed that the average atmospheric constant for Ouagadougou-Niger is a_1 = 0.626, a_2 = 0.7999 and the tuning constants is n_1 = 0.09835 and n_2 = 0.266. Also, the yearly atmospheric constants affirmed the lower atmosphere of Ouagadougou is very dynamic. Hence, it is recommended that radiosonde and weather station manufacturers should constantly review the atmospheric constant over a geographical location to enable about eighty percent data retrieval.

Keywords: aerosols retention, aerosols loading, statistics, analytical technique

Procedia PDF Downloads 315

Authors: Adilah Abdul Ghapor, Yong Zulina Zubairi, Rahmatullah Imon

Abstract:

Missing value problem is common in statistics and has been of interest for years. This article considers two modern techniques in handling missing data for linear functional relationship model (LFRM) namely the Expectation-Maximization (EM) algorithm and Expectation-Maximization with Bootstrapping (EMB) algorithm using three performance indicators; namely the mean absolute error (MAE), root mean square error (RMSE) and estimated biased (EB). In this study, we applied the methods of imputing missing values in the LFRM. Results of the simulation study suggest that EMB algorithm performs much better than EM algorithm in both models. We also illustrate the applicability of the approach in a real data set.

Keywords: expectation-maximization, expectation-maximization with bootstrapping, linear functional relationship model, performance indicators

Procedia PDF Downloads 399

Authors: Pantea Salehizadeh, Martin P. Bucknall, Robert Driscoll, Jayashree Arcot, George Srzednicki

Abstract:

Banana is one of the most important crops produced in large quantities in tropical and sub-tropical countries. Of the total plant material grown, approximately 40% is considered waste and left in the field to decay. This practice allows fungal diseases such as Sigatoka Leaf Spot to develop, limiting plant growth and spreading spores in the air that can cause respiratory problems in the surrounding population. The pseudostem is considered a waste residue of production (60 to 80 tonnes/ha/year), although it is a good source of dietary fiber and volatile organic compounds (VOC’s). Strategies to process banana pseudostem into palatable, nutritious and marketable food materials could provide significant social and economic benefits. Extraction of VOC’s with desirable odor from dried and fresh pseudostem could improve the smell of products from the confectionary and bakery industries. Incorporation of banana pseudostem flour into bakery products could provide cost savings and improve nutritional value. The aim of this study was to determine the effects of drying methods and different banana species on the profile of volatile aroma compounds in dried banana pseudostem. The banana species analyzed were Musa acuminata and Musa balbisiana. Fresh banana pseudostem samples were processed by either freeze-drying (FD) or heat pump drying (HPD). The extraction of VOC’s was performed at ambient temperature using vacuum distillation and the resulting, mostly aqueous, distillates were analyzed using headspace solid phase microextraction (SPME) gas chromatography – mass spectrometry (GC-MS). Optimal SPME adsorption conditions were 50 °C for 60 min using a Supelco 65 μm PDMS/DVB Stableflex fiber1. Compounds were identified by comparison of their electron impact mass spectra with those from the Wiley 9 / NIST 2011 combined mass spectral library. The results showed that the two species have notably different VOC profiles. Both species contained VOC’s that have been established in literature to have pleasant appetizing aromas. These included l-Menthone, D-Limonene, trans-linlool oxide, 1-Nonanol, CIS 6 Nonen-1ol, 2,6 Nonadien-1-ol, Benzenemethanol, 4-methyl, 1-Butanol, 3-methyl, hexanal, 1-Propanol, 2-methyl- acid، 2-Methyl-2-butanol. Results show banana pseudostem VOC’s are better preserved by FD than by HPD. This study is still in progress and should lead to the optimization of processing techniques that would promote the utilization of banana pseudostem in the food industry.

Keywords: heat pump drying, freeze drying, SPME, vacuum distillation, VOC analysis

Procedia PDF Downloads 335